An Introductory Handbook of Bayesian Thinking

As Bayesian techniques become more common across a variety of fields, it becomes important for experts in those fields to understand those methods. An Introductory Handbook of Bayesian Thinking brings Bayesian thinking and methods to a wide audience beyond the mathematical sciences. Appropriate for students with some background in calculus and introductory statistics, particularly for nonstatisticians with a sufficient mathematical background, the text provides a gentle introduction to Bayesian ideas with a wide array of supporting examples from a variety of fields.

• Utilizes real datasets to illustrate Bayesian models and their results
• Guides readers on coding Bayesian models using the statistical software R, including a helpful introduction and supporting online resource
• Appropriate for an undergraduate statistics course, as well as for non-statisticians with sufficient mathematical background (integral and differential Calculus and an introductory Statistics course)
• Covers any more advanced topics which readers may not be familiar with-such as the basic idea of vectors and matrices-as much as needed in order to foster understanding of core concepts